Abstract
Finding communities in structural networks (online social networks included) with sufficient accuracy is an important issue. We present a new method to identify communities that are in the same order of time complexity as the existing algorithms. In particular, we present an efficient algorithm using random walkers which, on a given network, generates a new network to better reveal the structures of the original network. We then use existing hierarchical clustering algorithms on the new network to find communities. We carry out simulations on both computer-generated data and the widely-used karate club data [10], and show that our algorithm can identify communities with much improved accuracy.
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Li, Y., Wang, J., Liu, B., Liang, Q. (2013). Finding Network Communities Using Random Walkers with Improved Accuracy. In: Du, DZ., Zhang, G. (eds) Computing and Combinatorics. COCOON 2013. Lecture Notes in Computer Science, vol 7936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38768-5_73
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DOI: https://doi.org/10.1007/978-3-642-38768-5_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38767-8
Online ISBN: 978-3-642-38768-5
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